The authors examine simultaneously the causal links connecting monetary policy variables, real activity, and stock returns. Their interest lies in the fact that the dynamics of asset prices can provide key insights—in terms of information—for the conduct of monetary policy, since asset prices constitute a class of potentially leading indicators of either economic activity or inflation. This is of particular interest in the context of an inflation-targeting regime, where the monetary policy stance is set according to inflation forecasts. While most empirical studies on causality have examined this issue using Granger's (1969) original definition, the authors examine the causality relations through the generalization proposed in Dufour and Renault (1998).

For the United States, the authors find no support for stock returns as a leading indicator of the macroeconomic variables considered, or for stock returns being influenced by those macroeconomic variables, except for one case: fluctuations in M1 tend to anticipate fluctuations in stock returns. Furthermore, the authors' empirical methodology allows them to infer that monetary aggregates may have significant predictive power for income and prices at longer horizons. It is therefore incorrect to dismiss the importance of monetary aggregates based on the usual Granger causality criteria. The causality pattern inferred by the authors' procedure is consistent with the Phillips curve (for the inflation dynamics) and with the Taylor rule in the case of the interest rate.

For Canada, the results are much different. The authors show that there is a potential role for asset prices as a predictor of some important macroeconomic variables, namely interest rates, inflation, and output at policy-relevant horizons. Furthermore, some measures of monetary aggregates tend to dominate the interest rate as robust causal variables for output growth and inflation. However, the authors do not find strong evidence in favour of the Phillips curve and the Taylor rule. Finally, for both Canada and the United States, the authors show that seasonal adjustments can highly distort the inferred causality structure.